Collaborative, participant-driven learning works!

Science has become a big-data endeavor. But scientists are not universally adept in “data science” — the computing and statistical skillsets needed to handle, sort, analyze and draw conclusions from big data. The shortage of know-how in data science can hamper research, medicine and even private industry

A new paper led by Daniela Huppenkothen, Associate Director of DiRAC, was just published in the Proceedings of the National Academy of Sciences on how we can learn these skills by working collaboratively. With a team of researchers from the University of Washington, New York University and the University of California, Berkeley she developed an interactive workshop in data science for researchers at multiple stages of their careers. The course format, called “hack week,” blends elements from both traditional lecture-style pedagogy with participant-driven projects. The most recent was a neuroscience-themed event held in July on the UW campus organized by Ariel Rokem, a data scientist with the UW eScience Institute. As the team reports in a their paper published Aug. 20, participants rated the hack weeks as opportunities to learn about new concepts, foster new connections, share data openly, and develop skills and work on problems that will positively affect their day-to-day research lives.